Bolted joints are critical in engineering for maintaining structural integrity and reliability. Accurate prediction of parameters influencing their function and behavior is essential for optimal performance. Traditional methods often fail to capture the non-linear behavior of bolted joints or require significant computational resources, limiting accuracy and efficiency. This study addresses these limitations by combining empirical data with a feed-forward neural network to predict load capacity and friction coefficients. Leveraging experimental data and systematic preprocessing, the model effectively captures nonlinear relationships, including rescaling output variables to address scale discrepancies, achieving 95.24% predictive accuracy. While limited dataset size and diversity restrict generalizability, the findings demonstrate the potential of neural networks as a reliable, efficient alternative for bolted joint design. Future work will focus on expanding datasets and exploring hybrid modeling techniques to enhance applicability.

翻译：螺栓连接在工程中对于维持结构完整性与可靠性至关重要。准确预测影响其功能与行为的参数是实现最优性能的关键。传统方法往往难以捕捉螺栓连接的非线性行为，或需要大量计算资源，限制了预测精度与效率。本研究通过将经验数据与前馈神经网络相结合，以预测承载能力与摩擦系数，从而应对这些局限性。利用实验数据与系统化预处理，该模型有效捕捉了非线性关系，包括通过重新标定输出变量以解决尺度差异问题，实现了95.24%的预测准确率。尽管数据集规模与多样性有限限制了模型的泛化能力，但研究结果证明了神经网络作为螺栓连接设计中可靠、高效替代方案的潜力。未来工作将集中于扩展数据集并探索混合建模技术，以提升其适用性。